path_to_ncdf
## [1] "./DataBaseOllieNew/ID103/Trip_W38/netCDF/"
### Now without max and min
Source: Ollie’s notebooks and email exchanges between Jelle, Ollie and me
The data are not calibrated yet.
Since there should be more or less 5-min segments of data every hour, the raw data at 10 Hz are first divided into 5 minute segments (or 15, but it will only contain 5)
Data cleaning is done on these segments. So, each segments:
Resampling is done at 1 Hz. It is basically done by fitting sinusoids to the data (Fast Fourier Transform) and then taking value points. We’re using the obspy.resample function in python which is a wrapper of scipy.signal.resample
Everything is saved into a netCDF file
## [1] "number of observations is 52840"
## [1] "summary diff time Pressure sensor -3.90526986122131"
## [2] "summary diff time Pressure sensor 0.0950310230255127"
## [3] "summary diff time Pressure sensor 0.103679895401001"
## [4] "summary diff time Pressure sensor 5.86721945856235"
## [5] "summary diff time Pressure sensor 0.103681087493896"
## [6] "summary diff time Pressure sensor 3575.79359984398"
## [1] "Min, 1st Qu, Median, Mean, 3rdQu, Max"
## group median_BaromP mean_BaromP min_BaromP max_BaromP first_Qu_BaromP
## 1 1 1004.6653 1004.6440 1004.2122 1005.3772 1004.5328
## 2 2 1002.8705 1002.8575 1002.1189 1003.7524 1002.7023
## 3 3 1002.4299 1002.4150 1001.7454 1002.9746 1002.2773
## 4 4 1003.1306 1003.1132 1002.5293 1003.4780 1003.0043
## 5 5 1003.4261 1003.4316 1002.9446 1003.9749 1003.2789
## 6 6 1004.2625 1004.2951 1003.8157 1004.7732 1004.1774
## 7 7 1004.2416 1004.2265 1003.7842 1004.6318 1004.0733
## 8 8 1003.7397 1003.7306 1003.1479 1004.0920 1003.6600
## 9 9 1004.3677 1004.3622 1004.0176 1004.7124 1004.2557
## 10 10 1003.9596 1003.9656 1003.7170 1004.3254 1003.8876
## 11 11 1002.9419 1002.9202 1002.3721 1003.3923 1002.7982
## 12 12 1002.7860 1002.7685 1002.3652 1003.1025 1002.6684
## 13 13 1002.3820 1002.3699 1001.7561 1002.7139 1002.2866
## 14 14 1001.7819 1001.7590 1001.1667 1002.2256 1001.6544
## 15 15 998.9103 998.8398 997.9465 999.6138 998.6870
## 16 16 998.1918 998.1742 997.4099 998.7659 998.0139
## 17 17 997.1760 997.1278 996.1445 997.8984 996.9027
## 18 18 995.4299 995.3964 994.6008 995.9795 995.2260
## 19 19 993.3605 993.3499 992.3938 994.0549 993.1247
## 20 20 994.6257 994.6029 993.8801 995.2537 994.4726
## 21 21 995.0159 994.9881 994.2246 995.5679 994.7762
## 22 22 994.5916 994.6104 993.8076 995.4504 994.4024
## 23 23 997.1234 997.1381 996.7314 997.5322 997.0500
## 24 24 998.9180 998.8961 998.3787 999.2344 998.8113
## 25 25 997.4033 997.3355 995.9983 998.3494 997.0769
## 26 26 999.4017 999.3457 997.9536 1000.2871 999.0873
## 27 27 1001.8823 1001.8612 1000.5496 1003.3369 1001.5768
## 28 28 1002.2783 1002.2655 1001.0762 1003.2444 1002.0182
## 29 29 1002.6190 1002.6052 1001.3147 1003.7219 1002.2726
## 30 30 1007.7781 1007.7753 1007.2732 1008.2400 1007.6375
## 31 31 1008.3719 1008.4174 1007.7971 1009.1785 1008.0426
## 32 32 1009.2290 1009.2204 1008.7954 1009.9858 1009.0562
## 33 33 1009.3678 1009.3626 1008.9194 1009.9106 1009.1977
## 34 34 1011.8485 1011.8344 1011.2307 1012.2993 1011.6919
## 35 35 1011.4658 1011.4486 1010.2695 1011.9580 1011.2794
## 36 36 1011.9304 1011.9261 1011.4639 1012.3103 1011.8364
## 37 37 1012.9419 1012.9805 1012.4658 1013.4380 1012.8140
## 38 38 1012.5321 1012.5391 1012.0256 1013.2788 1012.3542
## 39 39 1011.7008 1011.6844 1010.9854 1012.4717 1011.5140
## 40 40 1012.7853 1012.7828 1012.1404 1013.2415 1012.6357
## 41 41 1013.3151 1013.3212 1012.5684 1014.0115 1013.1492
## 42 42 1013.8501 1013.8362 1013.0852 1014.3425 1013.7438
## 43 43 1014.0856 1014.0607 1013.3084 1014.6895 1013.8542
## 44 44 1014.7897 1014.6845 1013.4592 1015.7068 1014.3518
## 45 45 1013.6835 1013.6907 1012.4895 1014.7820 1013.4565
## 46 46 1012.4397 1012.3952 1011.0823 1013.5925 1012.0532
## 47 47 1011.1542 1011.6022 1010.0823 1013.4072 1010.7939
## 48 48 1010.5264 1010.5243 1009.3691 1011.3926 1010.2457
## 49 49 1011.7343 1011.3505 1008.9204 1012.9363 1010.6258
## 50 50 1011.1517 1011.1519 1010.8206 1011.9014 1011.0673
## 51 51 1008.3342 1008.3391 1007.4824 1009.0598 1008.1262
## 52 52 1008.7609 1008.7738 1007.6838 1009.7104 1008.5352
## 53 53 1009.0854 1009.0874 1008.2161 1009.8049 1008.9376
## 54 54 1009.2548 1009.2590 1008.5054 1010.1555 1009.0369
## 55 55 1011.4419 1011.4457 1011.2229 1011.6702 1011.3687
## 56 56 1011.4747 1011.4764 1011.1755 1011.6980 1011.3911
## 57 57 1011.7526 1011.7636 1011.5620 1012.0408 1011.6915
## 58 58 1006.9847 1006.9936 1006.3569 1007.8494 1006.8605
## 59 59 1009.6101 1009.5948 1009.1448 1009.9458 1009.4978
## 60 60 1008.4142 1008.4048 1007.8416 1008.7490 1008.3085
## 61 61 1007.9767 1007.9703 1007.5239 1008.4062 1007.8861
## 62 62 1007.5713 1007.5502 1007.0750 1007.9416 1007.4241
## 63 63 1004.4175 1004.4087 1003.5076 1005.2822 1004.2389
## 64 64 1002.6390 1002.6521 1001.8411 1003.5325 1002.4518
## 65 65 1000.5936 1000.5947 999.9331 1001.3618 1000.3617
## 66 66 1000.7910 1000.7996 1000.1162 1001.5847 1000.7002
## 67 67 1000.6539 1000.8262 999.4133 1002.7859 1000.2472
## 68 68 1001.5499 1001.5547 1000.6243 1002.2144 1001.4041
## 69 69 1002.7961 1002.7633 1001.9321 1003.4392 1002.6199
## 70 70 1003.4053 1003.4384 1002.5566 1004.3945 1003.1971
## 71 71 1003.9114 1003.8865 1003.0679 1004.6426 1003.6039
## 72 72 1003.8633 1003.8667 1003.3010 1004.3906 1003.7192
## 73 73 1004.6531 1004.6062 1003.4670 1005.7766 1004.3568
## 74 74 1004.1903 1004.1938 1003.1802 1005.4719 1003.9152
## 75 75 1004.1471 1004.1104 1002.8108 1005.1462 1003.8542
## 76 76 1005.4268 1005.4445 1005.0120 1006.0896 1005.2820
## 77 77 1004.2435 1004.2773 1003.1672 1005.1206 1004.0133
## 78 78 1006.9924 1006.9947 1006.6199 1007.3301 1006.8511
## 79 79 1007.5394 1007.5438 1007.1772 1007.9468 1007.4238
## 80 80 1008.2933 1008.2945 1007.8618 1008.7136 1008.1820
## 81 81 1008.8339 1008.8643 1008.4202 1009.4573 1008.6703
## 82 82 1009.4902 1009.4666 1009.0164 1009.8796 1009.3362
## 83 83 1009.9581 1009.9592 1009.6191 1010.3491 1009.8644
## 84 84 1010.4524 1010.4235 1009.8118 1011.2412 1010.2355
## 85 85 1010.5406 1010.5107 1010.0559 1010.8503 1010.3958
## 86 86 1010.4788 1010.4633 1009.9944 1010.9248 1010.3486
## 87 87 1008.0751 1008.0451 1007.2651 1008.6812 1007.8428
## third_Qu_BaromP
## 1 1004.7482
## 2 1003.0333
## 3 1002.5707
## 4 1003.2342
## 5 1003.5823
## 6 1004.4196
## 7 1004.3576
## 8 1003.8279
## 9 1004.4574
## 10 1004.0320
## 11 1003.0594
## 12 1002.8802
## 13 1002.4667
## 14 1001.9010
## 15 999.0623
## 16 998.3361
## 17 997.3863
## 18 995.5835
## 19 993.6074
## 20 994.7507
## 21 995.2381
## 22 994.8642
## 23 997.2218
## 24 998.9816
## 25 997.6239
## 26 999.6238
## 27 1002.2001
## 28 1002.5410
## 29 1002.9688
## 30 1007.9195
## 31 1008.7542
## 32 1009.3698
## 33 1009.5032
## 34 1011.9606
## 35 1011.6060
## 36 1012.0357
## 37 1013.1712
## 38 1012.6917
## 39 1011.8504
## 40 1012.9582
## 41 1013.4960
## 42 1013.9590
## 43 1014.3070
## 44 1015.0262
## 45 1013.9606
## 46 1012.7321
## 47 1012.9458
## 48 1010.8339
## 49 1012.0367
## 50 1011.2206
## 51 1008.5444
## 52 1008.9871
## 53 1009.2637
## 54 1009.4696
## 55 1011.5181
## 56 1011.5775
## 57 1011.8287
## 58 1007.1331
## 59 1009.6957
## 60 1008.5134
## 61 1008.0586
## 62 1007.6687
## 63 1004.6107
## 64 1002.8386
## 65 1000.8241
## 66 1000.8992
## 67 1001.0088
## 68 1001.7152
## 69 1002.9146
## 70 1003.7113
## 71 1004.1539
## 72 1004.0121
## 73 1004.8704
## 74 1004.4413
## 75 1004.3860
## 76 1005.5816
## 77 1004.5398
## 78 1007.1339
## 79 1007.6723
## 80 1008.4004
## 81 1009.0342
## 82 1009.5884
## 83 1010.0413
## 84 1010.6050
## 85 1010.6316
## 86 1010.6177
## 87 1008.2537
## median_BaromP mean_BaromP min_BaromP max_BaromP first_Qu_BaromP
## 1 1005.435 1005.858 992.3938 1015.707 1002.604
## third_Qu_BaromP
## 1 1010.304
{r PressureTS2 , echo=FALSE} ## I was trying to do the plots here... but I have been stuck # ydex=which(Pressure_df3$group<19) # pmedian_BaromP <- ggplot() + geom_point(data=Pressure_df3[ydex,], aes(x=Time3, y= Pressure_hPa)) # geom_hline(aes(yintercept=median_BaromP,color='red')) # pmedian_BaromP #Source: Ollie’s notebooks, email exchanges between Jelle, Ollie and me, some reading and youtube videos
The data are calibrated.
Since there should be more or less 5-min segments of data every hour (could be 1), the raw data at 10 Hz are first divided into 5 minute segments (or 15, but it will only contain 5)
Data cleaning is done on these segments. So, each segments:
No resampling
For each segment, the power spectral density is estimated using Welch’s method; it basically divides data into overlapping segments, computing a modified periodogram for each segment and averaging the periodograms
The PSD data are transformed to obtain SPL as \(10 \times \log(PSD/(2*10^{-5})*2))\)
The SPL data are saved into a netCDF file
## [1] "number of observations is 57"
## [1] "summary diff time Pressure sensor 3602.6380739212"
## [2] "summary diff time Pressure sensor 3603.26880818605"
## [3] "summary diff time Pressure sensor 3603.66190612316"
## [4] "summary diff time Pressure sensor 5213.22480032274"
## [5] "summary diff time Pressure sensor 4504.71238946915"
## [6] "summary diff time Pressure sensor 28891.079985857"